Noisy Delay Denoises Biochemical Oscillators.

Genetic oscillations are generated by delayed transcriptional negative feedback loops, wherein repressor proteins inhibit their own synthesis after a temporal production delay. This delay is distributed because it arises from a sequence of noisy processes, including transcription, translocation, translation, and folding. Because the delay determines repression timing and, therefore, oscillation period, it has been commonly believed that delay noise weakens oscillatory dynamics. Here, we demonstrate that noisy delay can surprisingly denoise genetic oscillators. Specifically, moderate delay noise improves the signal-to-noise ratio and sharpens oscillation peaks, all without impacting period and amplitude. We show that this denoising phenomenon occurs in a variety of well-studied genetic oscillators, and we use queueing theory to uncover the universal mechanisms that produce it.

Tunable Dynamics in a Multistrain Transcriptional Pulse Generator.

One challenge in synthetic biology is the tuning of regulatory components within gene circuits to elicit a specific behavior. This challenge becomes more difficult in synthetic microbial consortia since each strain's circuit must function at the intracellular level and their combination must operate at the population level. Here we demonstrate that circuit dynamics can be tuned in synthetic consortia through the manipulation of strain fractions within the community. To do this, we construct a microbial consortium comprised of three strains of engineered Escherichia coli that, when cocultured, use homoserine lactone-mediated intercellular signaling to create a multistrain incoherent type-1 feedforward loop (I1-FFL). Like naturally occurring I1-FFL motifs in gene networks, this engineered microbial consortium acts as a pulse generator of gene expression. We demonstrate that the amplitude of the pulse can be easily tuned by adjusting the relative population fractions of the strains. We also develop a mathematical model for the temporal dynamics of the microbial consortium. This model allows us to identify population fractions that produced desired pulse characteristics, predictions that were confirmed for all but extreme fractions. Our work demonstrates that intercellular gene circuits can be effectively tuned simply by adjusting the starting fractions of each strain in the consortium.

A Rare Case of Moyamoya Disease in a Hispanic Woman: Unveiling Non-Asian Ethnicity and Atypical Risk Factors.

BACKGROUND Moyamoya disease is a rare and progressive cerebrovascular disorder caused by narrowed or blocked arteries supplying the brain. First described in Japan, the disease's incidence is higher in Asian countries and primarily affects children, although adults can also be afflicted. Following a literature review, very little was found regarding non-Asian ethnicities and the lack of typically associated risk factors that are known correlates of Moyamoya disease. CASE REPORT We present the case of a 41-year-old Hispanic woman with a history of type 1 diabetes mellitus and asthma who presented to the Emergency Department with concerns of recurrent transient episodes of left upper extremity weakness and paresthesia followed by confusion. The patient's blood pressure on arrival was 215/134 mmHg, and heart rate was 124 beats per min. Computed tomography of the head was unremarkable, but a computed tomography angiogram of the head demonstrated several areas of severe and bilateral stenosis with radiographic appearances, suggestive of Moyamoya disease. Magnetic resonance imaging of the brain would later illustrate two 6×2-mm ischemic infarcts in the right posterior centrum semiovale. CONCLUSIONS Moyamoya disease in the non-Asian population is rarely reported. We present a case of this condition in a patient of Hispanic ethnicity. Although it is generally considered a non-atherosclerotic disease, some literature suggests that atherosclerotic disease may also contribute to the development and possible acceleration of clinical features of Moyamoya disease. Given our patient's risk factors, we postulated that our patient's presentation was likely multifactorial, with both non-sclerotic and atherosclerotic disease.

Signaling in microbial communities with open boundaries.

Microbial communities such as swarms or biofilms often form at the interfaces of solid substrates and open fluid flows. At the same time, in laboratory environments these communities are commonly studied using microfluidic devices with media flows and open boundaries. Extracellular signaling within these communities is therefore subject to different constraints than signaling within classic, closed-boundary systems such as developing embryos or tissues, yet is understudied by comparison. Here, we use mathematical modeling to show how advective-diffusive boundary flows and population geometry impact cell-cell signaling in monolayer microbial communities. We reveal conditions where the intercellular signaling lengthscale depends solely on the population geometry and not on diffusion or degradation, as commonly expected. We further demonstrate that diffusive coupling with the boundary flow can produce signal gradients within an isogenic population, even when there is no flow within the population. We use our theory to provide new insights into the signaling mechanisms of published experimental results, and we make several experimentally verifiable predictions. Our research highlights the importance of carefully evaluating boundary dynamics and environmental geometry when modeling microbial cell-cell signaling and informs the study of cell behaviors in both natural and synthetic systems.

Pneumocephalus secondary to epidural analgesia: a case report.

INTRODUCTION: Epidural anesthesia is commonly used for analgesia during labor, and headache is a common complaint following this procedure. Pneumocephalus, on the other hand, is a rare and potentially serious complication of epidural anesthesia, which is most often caused by accidental puncture of the dura with the introduction of air into intrathecal space. CASE PRESENTATION: We present the case of a 19-year-old Hispanic female who developed a severe frontal headache and neck pain eight hours following epidural catheter placement to deliver analgesia during labor. Physical examination was within normal limits without any neurological deficits. Computed tomography of the head and neck would later demonstrate small to moderate amounts of pneumocephalus, predominantly within the frontal horn of the lateral ventricles, and a moderate amount of air within the spinal canal. She was treated conservatively with analgesia. Though headache recurred after discharge, repeat imaging showed improvement in the volume of pneumocephalus and conservative management was continued. CONCLUSIONS: Although a rare complication and an uncommon cause of headache following epidural anesthesia, a high index of suspicion must remain for pneumocephalus as it may cause significant morbidity and, in some cases, be potentially life-threatening.

Acquired Long QT Syndrome: Ventricular Fibrillation in an Otherwise Healthy Young Female.

Long QT syndrome (LQTS) occurs when there is an abnormality of myocardial repolarization characterized specifically by a prolonged QT interval on an electrocardiogram (ECG). This can be particularly dangerous as it is associated with an increased risk of polymorphic ventricular tachycardia and a life-threatening arrhythmia otherwise known as torsades de pointes (TdP). We present a case of a 40-year-old Indian female whose medical history was significant only for anemia and depression/anxiety that presented in a ventricular fibrillation cardiac arrest after becoming dyspneic and light-headed while dancing. Of relevance, she was taking sertraline 50mg once daily, a class of medications known to prolong the QT interval as well as having low serum calcium on presentation. Both her initial and subsequent electrocardiograms illuminated significantly prolonged QTc intervals. She subsequently sustained a ventricular tachycardia cardiac arrest, which degenerated into ventricular fibrillation in the cardiac intensive care unit two days later. Ultimately, the patient was pronounced brain-dead by the end of the week. We concluded this to be a case of LQTS predisposing to TdP, which then would degenerate into ventricular fibrillation. This case highlights multiple risk factors that are known to predispose to the aforementioned etiology. Further research is needed not only on common medications and their dose-dependent relationship on the QT interval across different ethnic groups but also on educating providers regarding multiple risk factors they may or may not have the power to control.

A Complicated Clinical Course of Community-Acquired Staphylococcus aureus Infective Endocarditis.

Infective endocarditis (IE) is an infection of the heart's endocardial surface, heart valves, or implanted cardiac devices, with the most common causative organism being Staphylococcus aureus. The clinical presentation of IE can be variable, with some patients presenting with multisystemic complications, including renal, pulmonary, cutaneous, and neurologic complications. Cerebral infarction is the most common complication of IE. Here we present a case of a young male with S. aureus IE of a native cardiac valve who developed multiple complications during his clinical course.

Signaling in microbial communities with open boundaries.

Microbial communities such as swarms or biofilms often form at the interfaces of solid substrates and open fluid flows. At the same time, in laboratory environments these communities are commonly studied using microfluidic devices with media flows and open boundaries. Extracellular signaling within these communities is therefore subject to different constraints than signaling within classic, closed-boundary systems such as developing embryos or tissues, yet is understudied by comparison. Here, we use mathematical modeling to show how advective-diffusive boundary flows and population geometry impact cell-cell signaling in monolayer microbial communities. We reveal conditions where the intercellular signaling lengthscale depends solely on the population geometry and not on diffusion or degradation, as commonly expected. We further demonstrate that diffusive coupling with the boundary flow can produce signal gradients within an isogenic population, even when there is no flow within the population. We use our theory to provide new insights into the signaling mechanisms of published experimental results, and we make several experimentally verifiable predictions. Our research highlights the importance of carefully evaluating boundary dynamics and environmental geometry when modeling microbial cell-cell signaling and informs the study of cell behaviors in both natural and synthetic systems.

Delay-induced uncertainty in the glucose-insulin system: Pathogenicity for obesity and type-2 diabetes mellitus.

We have recently shown that physiological delay can induce a novel form of sustained temporal chaos we call delay-induced uncertainty (DIU) (Karamched et al. (Chaos, 2021, 31, 023142)). This paper assesses the impact of DIU on the ability of the glucose-insulin system to maintain homeostasis when responding to the ingestion of meals. We address two questions. First, what is the nature of the DIU phenotype? That is, what physiological macrostates (as encoded by physiological parameters) allow for DIU onset? Second, how does DIU impact health? We find that the DIU phenotype is abundant in the space of intrinsic parameters for the Ultradian glucose-insulin model-a model that has been successfully used to predict glucose-insulin dynamics in humans. Configurations of intrinsic parameters that correspond to high characteristic glucose levels facilitate DIU onset. We argue that DIU is pathogenic for obesity and type-2 diabetes mellitus by linking the statistical profile of DIU to the glucostatic theory of hunger.

Gastric Volvulus, An Important Yet Commonly Overlooked Etiology of Upper Gastrointestinal Bleeding: A Case Study.

Gastric volvulus is a distinct and uncommon pathology that usually presents with vomiting secondary to gastric outlet obstruction and gastrointestinal bleeding with an association with hiatal hernia. We present a case of a 71-year-old female who presented to the emergency department (ED) with a three-day history of coffee ground emesis. Of note, the patient was recently in the hospital under medical observation two weeks prior, with similar complaints of hematemesis. Chest X-ray revealed a left basilar opacity representing bowel gas suggestive of a hiatal hernia. Intravenous proton pump inhibitors were initiated but due to persistent recurrence of symptoms and progressive discomfort, a computed tomography (CT) of the chest and abdomen was ordered. This revealed a partial gastric volvulus with signs suggestive of vascular compromise of the herniated part of the stomach. She subsequently underwent emergent laparotomy, repair of the hiatal hernia, and partial gastrectomy and gastropexy. Post-surgical biopsy findings showed focal mucosal necrosis and ulceration, focal foveolar hyperplasia, edematous changes, and overall congestion in the submucosal tissue. She was discharged five days later with no complications or recurrence of symptoms.

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia.

The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain's division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains-but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.

Delay-induced uncertainty for a paradigmatic glucose-insulin model.

Medical practice in the intensive care unit is based on the assumption that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that delay within the glucose-insulin system can induce sustained temporal chaos, rendering the system unpredictable. Specifically, we exhibit such chaos for the ultradian glucose-insulin model. This well-validated, finite-dimensional model represents feedback delay as a three-stage filter. Using the theory of rank one maps from smooth dynamical systems, we precisely explain the nature of the resulting delay-induced uncertainty (DIU). We develop a framework one may use to diagnose DIU in a general oscillatory dynamical system. For infinite-dimensional delay systems, no analog of the theory of rank one maps exists. Nevertheless, we show that the geometric principles encoded in our DIU framework apply to such systems by exhibiting sustained temporal chaos for a linear shear flow. Our results are potentially broadly applicable because delay is ubiquitous throughout mathematical physiology.

Heterogeneity Improves Speed and Accuracy in Social Networks.

How does temporally structured private and social information shape collective decisions? To address this question we consider a network of rational agents who independently accumulate private evidence that triggers a decision upon reaching a threshold. When seen by the whole network, the first agent's choice initiates a wave of new decisions; later decisions have less impact. In heterogeneous networks, first decisions are made quickly by impulsive individuals who need little evidence to make a choice but, even when wrong, can reveal the correct options to nearly everyone else. We conclude that groups comprised of diverse individuals can make more efficient decisions than homogenous ones.

Majority sensing in synthetic microbial consortia.

As synthetic biocircuits become more complex, distributing computations within multi-strain microbial consortia becomes increasingly beneficial. However, designing distributed circuits that respond predictably to variation in consortium composition remains a challenge. Here we develop a two-strain gene circuit that senses and responds to which strain is in the majority. This involves a co-repressive system in which each strain produces a signaling molecule that signals the other strain to down-regulate production of its own, orthogonal signaling molecule. This co-repressive consortium links gene expression to ratio of the strains rather than population size. Further, we control the cross-over point for majority via external induction. We elucidate the mechanisms driving these dynamics by developing a mathematical model that captures consortia response as strain fractions and external induction are varied. These results show that simple gene circuits can be used within multicellular synthetic systems to sense and respond to the state of the population.

Bayesian inference of distributed time delay in transcriptional and translational regulation.

MOTIVATION: Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. RESULTS: We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. AVAILABILITY AND IMPLEMENTATION: Accompanying code in R is available at https://github.com/cbskust/DDE_BD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spatiotemporal Dynamics of Synthetic Microbial Consortia in Microfluidic Devices.

Synthetic microbial consortia consist of two or more engineered strains that grow together and share the same resources. When intercellular signaling pathways are included in the engineered strains, close proximity of the microbes can generate complex dynamic behaviors that are difficult to obtain using a single strain. However, when a consortium is not cultured in a well-mixed environment the constituent strains passively compete for space as they grow and divide, complicating cell-cell signaling. Here, we explore the temporal dynamics of the spatial distribution of consortia cocultured in microfluidic devices. To do this, we grew two different strains of Escherichia coli in microfluidic devices with cell-trapping regions (traps) of several different designs. We found that the size of the traps is a critical determinant of spatiotemporal dynamics. In small traps, cells can easily signal one another, but the relative proportion of each strain within the trap can fluctuate wildly. In large traps, the relative ratio of strains is stabilized, but intercellular signaling can be hindered by distances between cells. This presents a trade-off between the trap size and the effectiveness of intercellular signaling, which can be mitigated by increasing the initial seeding of cells in larger traps. We also built a mathematical model, which suggests that increasing the number of seed cells can also increase the strain ratio variability due to an increased number of strain interfaces in the trap. These results help elucidate the complex behaviors of synthetic microbial consortia in microfluidic traps and provide a means of analysis to help remedy the spatial heterogeneity inherent to different trap types.

Predicting Transcriptional Output of Synthetic Multi-input Promoters.

Recent advances in synthetic biology have led to a wealth of well-characterized genetic parts. As parts libraries grow, so too does the potential to create novel multi-input promoters that integrate disparate signals to determine transcriptional output. Our ability to construct such promoters will outpace our ability to characterize promoter performance, due to the vast number of input combinations. In this study, we examine the input-output relations of recently developed synthetic multi-input promoters and describe two methods for predicting their behavior. The first method uses 1-dimensional induction data obtained from experiments on single-input systems to predict the n-dimensional induction responses of systems with n inputs. We demonstrate that this approach accurately predicts Boolean (on/off) responses of multi-input systems consisting of novel chimeric transcription factors and hybrid promoters in Escherichia coli. The second method uses only a small amount of multi-input response data to accurately predict analog system response over the entire landscape of input combinations. Taken together, these methods facilitate the design of synthetic circuits that utilize multi-input promoters.

Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors.

One challenge for synthetic biologists is the predictable tuning of genetic circuit regulatory components to elicit desired outputs. Gene expression driven by ligand-inducible transcription factor systems must exhibit the correct ON and OFF characteristics: appropriate activation and leakiness in the presence and absence of inducer, respectively. However, the dynamic range of a promoter (i.e., absolute difference between ON and OFF states) is difficult to control. We report a method that tunes the dynamic range of ligand-inducible promoters to achieve desired ON and OFF characteristics. We build combinatorial sets of AraC-and LasR-regulated promoters containing -10 and -35 sites from synthetic and Escherichia coli promoters. Four sequence combinations with diverse dynamic ranges were chosen to build multi-input transcriptional logic gates regulated by two and three ligand-inducible transcription factors (LacI, TetR, AraC, XylS, RhlR, LasR, and LuxR). This work enables predictable control over the dynamic range of regulatory components.

Modeling mechanical interactions in growing populations of rod-shaped bacteria.

Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cell's environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.

Effects of cell cycle noise on excitable gene circuits.

We assess the impact of cell cycle noise on gene circuit dynamics. For bistable genetic switches and excitable circuits, we find that transitions between metastable states most likely occur just after cell division and that this concentration effect intensifies in the presence of transcriptional delay. We explain this concentration effect with a three-states stochastic model. For genetic oscillators, we quantify the temporal correlations between daughter cells induced by cell division. Temporal correlations must be captured properly in order to accurately quantify noise sources within gene networks.